Digital Communications and Networks最新文献

A blockchain-based user-centric identity management toward 6G networks 面向6G网络的基于区块链的用户中心身份管理

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.05.009

Guoqiang Zhang, Qiwei Hu, Yu Zhang, Tao Jiang

The developing Sixth-Generation (6G) network aims to establish seamless global connectivity for billions of humans, machines, and devices. However, the rich digital service and the explosive heterogeneous connection between various entities in 6G networks can not only induce increasing complications of digital identity management, but also raise material concerns about the security and privacy of the user identity. In this paper, we design a user-centric identity management that returns the sole control to the user self and achieves identity sovereignty toward 6G networks. Specifically, we propose a blockchain-based Identity Management (IDM) architecture for 6G networks, which provides a practical method to secure digital identity management. Subsequently, we develop a fully privacy-preserving identity attribute management scheme by using zero-knowledge proof to protect the privacy-sensitive identity attribute. In particular, the scheme achieves an identity attribute hiding and verification protocol to support users in obtaining and applying their identity attributes without revealing concrete data. Finally, we analyze the security of the proposed architecture and implement a prototype system to evaluate its performance. The results demonstrate that our architecture ensures effective user digital identity management in 6G networks.

正在发展的第六代（6G）网络旨在为数十亿人、机器和设备建立无缝的全球连接。然而，6G网络中丰富的数字业务和各种实体之间的爆炸性异构连接，不仅会导致数字身份管理的复杂性日益增加，而且会引发用户身份安全和隐私方面的实质性担忧。在本文中，我们设计了一种以用户为中心的身份管理，将唯一的控制权交还给用户自己，实现了对6G网络的身份主权。具体来说，我们提出了一种基于区块链的6G网络身份管理（IDM）架构，它提供了一种实用的方法来保护数字身份管理。随后，我们利用零知识证明来保护隐私敏感的身份属性，提出了一种完全保护隐私的身份属性管理方案。特别是，该方案实现了身份属性隐藏和验证协议，支持用户在不泄露具体数据的情况下获取和应用自己的身份属性。最后，我们分析了所提出的架构的安全性，并实现了一个原型系统来评估其性能。结果表明，我们的架构确保了6G网络中有效的用户数字身份管理。

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引用次数: 0

Enhanced multi-agent deep reinforcement learning for efficient task offloading and resource allocation in vehicular networks 基于多智能体深度强化学习的车辆网络高效任务卸载和资源分配

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.09.002

Long Xu , Jiale Tan , Hongcheng Zhuang

In response to the rising demand for low-latency, computation-intensive applications in vehicular networks, this paper proposes an adaptive task offloading approach for Vehicle-to-Everything (V2X) environments. Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm with an attention mechanism, the proposed approach optimizes computation offloading and resource allocation, aiming to minimize energy consumption and service delay. In this paper, vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links. The adaptive attention mechanism enables the system to prioritize relevant state information, leading to faster convergence. Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG (AT-MADDPG) algorithm significantly improves performance, achieving notable reductions in both energy consumption and latency compared to baseline algorithms, especially in high-demand, dynamic scenarios.

针对车载网络对低延迟、计算密集型应用日益增长的需求，本文提出了一种适用于V2X （Vehicle-to-Everything）环境的自适应任务卸载方法。该方法利用增强型多智能体深度确定性策略梯度（madpg）算法和关注机制，优化计算卸载和资源分配，以最小化能耗和服务延迟为目标。在本文中，车辆通过V2V链路将计算密集型任务动态地卸载给附近车辆，并通过V2I链路将计算密集型任务动态地卸载给路边单元。自适应关注机制使系统能够优先考虑相关状态信息，从而加快收敛速度。在现实的城市V2X场景中进行的模拟表明，与基线算法相比，所提出的注意力增强型MADDPG （AT-MADDPG）算法显著提高了性能，显著降低了能耗和延迟，特别是在高需求的动态场景中。

引用次数: 0

RIS-assisted cellular networks with multiple D2D pairs: Outage and ergodic achievable rate 具有多个D2D对的ris辅助蜂窝网络：中断和遍历可达率

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.08.008

Yaxuan Liu , Yiyang Ni , Haitao Zhao , Yuxi Wang , Yan Cai

Reconfigurable Intelligent Surface (RIS) is envisioned as a promising technology to improve the system capacity of 6G network, by controlling the electromagnetic wave propagation. Most existing works use the Central Limit Theorem (CLT) to analyze the performance of RIS-assisted systems for large number of reflective elements. However, the assumption of extremely large number of elements may not be practical in the actual situation. In addition, the CLT-based approximation yields an inaccurate scaling law of the outage probability when the transmit Signal-to-Noise Ratio (SNR) tends to infinity. Motivated by these limitations, in this paper, we investigate the performance of RIS-assisted cellular networks with multiple Device-to-Device (D2D) users under the general fading channels, i.e., Nakagami-m fading channels. We propose a tractable solution to evaluate the outage probability and the ergodic achievable rate, which is accurate for any number of reflective elements, any network topology, as well as any SNR. In addition, the accurate approximations for the high SNR case and the large number of reflective elements case are further derived in simpler closed form. Numerical results verify the accuracy of our analytical results and analyze the performance between CLT and the proposed method.

可重构智能表面（RIS）被认为是通过控制电磁波传播来提高6G网络系统容量的一项有前途的技术。现有的研究大多使用中心极限定理（CLT）来分析具有大量反射元素的ris辅助系统的性能。但是，在实际情况中，元素数量极大的假设可能并不实际。此外，当传输信噪比（SNR）趋于无穷大时，基于clt的近似产生了不准确的中断概率缩放规律。基于这些限制，在本文中，我们研究了具有多设备对设备（D2D）用户的ris辅助蜂窝网络在一般衰落信道下的性能，即Nakagami-m衰落信道。我们提出了一种易于处理的解决方案来评估中断概率和遍历可达率，该解决方案对任何数量的反射元件，任何网络拓扑以及任何信噪比都是准确的。此外，进一步以较简单的封闭形式推导出高信噪比情况和大量反射元情况的精确近似。数值结果验证了我们的分析结果的准确性，并分析了CLT与所提方法的性能差异。

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引用次数: 0

Single-channel blind source separation empowered joint transceiver optimization for wireless communications using deep learning 单通道盲源分离支持使用深度学习的无线通信联合收发器优化

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.04.008

Pengcheng Guo , Fuqiang Yao , Miao Yu , Cheng Li , Yanqun Tang , Zhaolong Ning

To tackle the physical layer security challenges in wireless communication, this paper introduces a multi-user architecture that leverages single-channel blind source separation, centered around a Multi-source Signal Mixture Separator (MSMS). This architecture consists of a multi-user encoder, a channel layer, and a separation decoder, allowing it to handle multiple functions simultaneously, including encoding, modulation, signal separation, demodulation, and decoding. The MSMS receiver effectively enables the separation of numerous user signals, making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals, thus significantly enhancing communication security. The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design. The design of a single receiver reduces system costs and improves spectrum efficiency. The MSMS outperforms traditional Space-time Block Coding (STBC) strategies regarding separation performance, particularly in Block Error Rate (BLER) metrics. Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation. Moreover, this study extends the MSMS framework from a two-user scenario to a three-user scenario, further demonstrating the flexibility and scalability of the proposed architecture.

为了解决无线通信中的物理层安全挑战，本文介绍了一种以多源信号混合分离器（MSMS）为中心，利用单通道盲源分离的多用户架构。该架构由一个多用户编码器、一个信道层和一个分离解码器组成，允许它同时处理多种功能，包括编码、调制、信号分离、解调和解码。MSMS接收机有效地实现了大量用户信号的分离，使得未经授权的窃听者极难从混合信号中提取有价值的信息，从而大大提高了通信的安全性。通过采用基于深度学习的网络设计，MSMS可以解决少量样本训练的挑战，并在传输过程中实现联合优化。单接收机的设计降低了系统成本，提高了频谱效率。MSMS在分离性能方面优于传统的空时分组编码（STBC）策略，特别是在分组错误率（BLER）指标方面。调制星座图进一步分析了多源信号混合分离的有效性。此外，本研究将MSMS框架从双用户场景扩展到三用户场景，进一步展示了所提出架构的灵活性和可扩展性。

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引用次数: 0

Distributed unsupervised meta-learning algorithm over multi-agent systems 多智能体系统的分布式无监督元学习算法

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2024.08.006

Zhenzhen Wang , Bing He , Zixin Jiang , Xianyang Zhang , Haidi Dong , Di Ye

Multi-Agent Systems (MAS), which consist of multiple interacting agents, are crucial in Cyber-Physical Systems (CPS), because they improve system adaptability, efficiency, and robustness through parallel processing and collaboration. However, most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents. Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference, represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks, increasing adaptability and reducing sample requirements. Inspired by these advancements, we propose a novel Distributed Unsupervised Meta-Learning (DUML) framework based on Meta-GMVAE and a fusion strategy. Furthermore, we present a DUML algorithm based on Gaussian Mixture Model (DUMLGMM), where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm. Simulations on Omniglot and MiniImageNet datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.

多智能体系统（MAS）由多个相互作用的智能体组成，通过并行处理和协作提高系统的适应性、效率和鲁棒性，在网络物理系统（CPS）中起着至关重要的作用。然而，大多数现有的无监督元学习方法是集中式的，不适合多智能体系统，其中数据是分布式存储的，并且所有智能体都无法访问。元gmvae基于变分自编码器（VAE）和集合级变分推理，代表了一种复杂的无监督元学习模型，通过有效地学习各种任务中的数据表示，提高适应性和减少样本需求，提高了生成性能。受这些进展的启发，我们提出了一种基于Meta-GMVAE和融合策略的分布式无监督元学习（DUML）框架。在此基础上，提出了一种基于高斯混合模型（DUMLGMM）的DUML算法，其中高斯混合模型的参数由期望最大化算法求解。在Omniglot和MiniImageNet数据集上的仿真表明，DUMLGMM可以达到相应集中式算法的性能，并且优于非协作算法。

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引用次数: 0

Double-side delay alignment modulation for multi-user millimeter wave and terahertz communications 用于多用户毫米波和太赫兹通信的双面延迟对准调制

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.04.002

Xingwei Wang , Haiquan Lu , Jieni Zhang , Yong Zeng

Delay Alignment Modulation (DAM) is an innovative broadband modulation technique well-suited for millimeter Wave (mmWave) and Terahertz (THz) massive Multiple-Input Multiple-Output (MIMO) communication systems. Leveraging the high spatial resolution and sparsity of multi-path channels, DAM effectively mitigates Inter-Symbol Interference (ISI) by aligning all multi-path components through a combination of delay pre-compensation (or post-compensation) and path-based beamforming. As such, ISI is eliminated while preserving multi-path power gains. In this paper, we investigate multi-user double-side DAM, which incorporates both delay pre-compensation at the transmitter and post-compensation at the receiver, in contrast to prior works that primarily focus on single-side DAM with only delay pre-compensation. Firstly, we derive the constraint on the number of introduced delays and formulate the corresponding delay pre/post-compensation vectors tailored for multi-user double-side DAM, given a specific number of delay compensations. Furthermore, we demonstrate that when the number of Base Stations (BSs)/User Equipment (UE) antennas is sufficiently large, single-side DAM—where delay compensation is performed only at the BS/UE—is preferable to double-side DAM, since the former results in less ISI to be spatially eliminated. Next, we propose two low-complexity path-based beamforming strategies based on the eigen-beamforming transmission and ISI-Zero Forcing (ZF), respectively. On this basis, we further analyze the achievable sum rates. Simulation results verify that with a sufficiently large number of BS/UE antennas, single-side DAM is adequate for ISI elimination. Moreover, compared to the benchmarking scheme of Orthogonal Frequency Division Multiplexing (OFDM), multi-user BS-side DAM achieves higher spectral efficiency and lower Peak-to-Average Power Ratio (PAPR).

延迟对准调制（DAM）是一种创新的宽带调制技术，非常适合于毫米波（mmWave）和太赫兹（THz）大规模多输入多输出（MIMO）通信系统。利用多径信道的高空间分辨率和稀疏性，DAM通过结合延迟预补偿（或后补偿）和基于路径的波束形成来对齐所有多径分量，有效地减轻了符号间干扰（ISI）。因此，在保留多路径功率增益的同时消除了ISI。在本文中，我们研究了多用户双面DAM，它同时包含了发送端的延迟预补偿和接收端的后补偿，而不是之前的工作主要集中在只有延迟预补偿的单边DAM。首先，我们推导了引入延迟数量的约束，并在给定特定延迟补偿数量的情况下，为多用户双侧DAM制定了相应的延迟补偿前/补偿后向量。此外，我们证明，当基站(BSs)/用户设备（UE）天线的数量足够大时，单边DAM -仅在BS/UE上进行延迟补偿-比双面DAM更可取，因为前者导致较少的ISI在空间上消除。接下来，我们分别提出了基于本征波束形成传输和基于isi零强迫（ZF）的两种低复杂度路径波束形成策略。在此基础上，进一步分析了可实现的求和速率。仿真结果表明，在BS/UE天线数量足够大的情况下，单面DAM足以消除ISI。此外，与正交频分复用（OFDM）的基准测试方案相比，多用户宽带侧DAM具有更高的频谱效率和更低的峰均功率比（PAPR）。

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引用次数: 0

Heterogeneous resource allocation with latency guarantee for computing power network 基于时延保证的计算能力网络异构资源分配

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.03.011

Ailing Zhong , Dapeng Wu , Boran Yang , Ruyan Wang

Computing Power Network (CPN) is a new paradigm that integrates communication, computing, and storage resources to provide services for tasks. However, tasks composed of non-independent subtasks have a preference for the resources required at each stage, which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services. Motivated by this, this paper jointly optimizes the full-service cycle of tasks, including transmission, task partitioning, and offloading. First, the transmission bandwidth is dynamically configured based on delay sensitivity of tasks. Second, with the real-time information from edge resource clusters and state resource clusters in the network, the optimal partitioning for a computation task is derived. Third, personalized resource allocation schemes are customized for computation and storage tasks respectively. Finally, the impact of resource parameter configuration on the latency violation probability of CPN is revealed. Moreover, compared with the benchmark schemes, our proposed scheme reduces the network latency violation probability by up to 1.17 × in the same network setting.

CPN （Computing Power Network）是一种集成通信、计算和存储资源，为任务提供服务的新范式。然而，由非独立子任务组成的任务对每个阶段所需的资源具有优先性，这增加了异构资源分配的难度，降低了CPN服务的延迟性能。基于此，本文对任务的全服务周期进行了优化，包括传输、任务分区和卸载。首先，根据任务的延迟敏感性动态配置传输带宽。其次，利用网络中边缘资源集群和状态资源集群的实时信息，推导出计算任务的最优划分；第三，分别针对计算任务和存储任务定制个性化的资源分配方案。最后，揭示了资源参数配置对CPN延迟违反概率的影响。此外，与基准方案相比，在相同的网络设置下，我们提出的方案将网络延迟违反概率降低了1.17倍。

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引用次数: 0

V-track: Blockchain-enabled IoT system for reliable vehicle location verification V-track：支持区块链的物联网系统，用于可靠的车辆位置验证

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2024.08.004

Mritunjay Shall Peelam, Kunjan Shah, Vinay Chamola

Location-Based Services (LBS) have greatly improved efficiency and functionality in various domains, but privacy and security concerns remain due to the centralized nature of many existing systems. To address these issues, this paper introduces the V-Track system, a decentralized architecture using blockchain technology for reliable vehicle location verification. By integrating GPS devices (SparkFun GPS NEO-M9), IoT-enabled sensors, and a Cosmos blockchain-based ledger (network of interconnected blockchains), V-Track aims to solve centralized LBS problems. Through rigorous simulation experiments, this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy. This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems, enhancing reliability and trustworthiness through blockchain integration, improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy, and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology. These advancements hold promise for applications across multiple sectors, including logistics, supply chain management, urban planning, and emerging fields such as autonomous vehicles and augmented reality.

基于位置的服务（LBS）在各个领域极大地提高了效率和功能，但是由于许多现有系统的集中化特性，隐私和安全问题仍然存在。为了解决这些问题，本文引入了V-Track系统，这是一种使用区块链技术进行可靠车辆位置验证的分散架构。通过集成GPS设备（SparkFun GPS NEO-M9）、支持物联网的传感器和基于Cosmos区块链的分类账（相互连接的区块链网络），V-Track旨在解决集中式LBS问题。通过严格的仿真实验，本文评估了V-Track系统的性能和安全性，并展示了其在保护用户隐私的同时提供可靠位置验证的潜力。本文提出了V-Track作为集中式LBS隐私和安全问题的分散解决方案，通过区块链集成提高可靠性和可信度，改进GPS设备和物联网传感器的跟踪机制以提高准确性，并通过其分散设计和使用区块链技术提供集中式LBS的隐私保护替代方案。这些进步有望应用于多个领域，包括物流、供应链管理、城市规划以及自动驾驶汽车和增强现实等新兴领域。

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引用次数: 0

Secformer: Privacy-preserving atomic-level componentized transformer-like model with MPC Secformer：具有MPC的保护隐私的原子级组件化变压器模型

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.04.009

Chi Zhang , Tao Shen , Fenhua Bai , Kai Zeng , Xiaohui Zhang , Bin Cao

The global surge in Artificial Intelligence (AI) has been triggered by the impressive performance of deep-learning models based on the Transformer architecture. However, the efficacy of such models is increasingly dependent on the volume and quality of data. Data are often distributed across institutions and companies, making cross-organizational data transfer vulnerable to privacy breaches and subject to privacy laws and trade secret regulations. These privacy and security concerns continue to pose major challenges to collaborative training and inference in multi-source data environments. These challenges are particularly significant for Transformer models, where the complex internal encryption computations drastically reduce computational efficiency, ultimately threatening the model's practical applicability. We hence introduce Secformer, an innovative architecture specifically designed to protect the privacy of Transformer-like models. Secformer separates the encoder and decoder modules, enabling the decomposition of computation flows in Transformer-like models and their efficient mapping to Multi-Party Computation (MPC) protocols. This design effectively addresses privacy leakage issues during the collaborative computation process of Transformer models. To prevent performance degradation caused by encrypted attention modules, we propose a modular design strategy that optimizes high-level components by reconstructing low-level operators. We further analyze the security of Secformer's core components, presenting security definitions and formal proofs. We construct a library of fundamental operators and core modules using atomic-level component designs as the basic building blocks for encoders and decoders. Moreover, these components can serve as foundational operators for other Transformer-like models. Extensive experimental evaluations demonstrate Secformer's excellent performance while preserving privacy and offering universal adaptability for Transformer-like models.

基于Transformer架构的深度学习模型令人印象深刻的表现引发了人工智能（AI）的全球激增。然而，这些模型的有效性越来越依赖于数据的数量和质量。数据通常分布在机构和公司之间，这使得跨组织的数据传输容易受到隐私泄露的影响，并受到隐私法和商业秘密法规的约束。这些隐私和安全问题继续给多源数据环境中的协作训练和推理带来重大挑战。这些挑战对于Transformer模型来说尤其重要，其中复杂的内部加密计算大大降低了计算效率，最终威胁到模型的实际适用性。因此，我们介绍Secformer，一种创新的架构，专门用于保护类似变形金刚的模型的隐私。Secformer将编码器和解码器模块分开，使计算流在类似transformer的模型中分解，并有效地映射到多方计算（MPC）协议。该设计有效地解决了Transformer模型协同计算过程中的隐私泄露问题。为了防止加密注意力模块导致的性能下降，我们提出了一种模块化设计策略，通过重构低级操作符来优化高级组件。我们进一步分析了Secformer核心组件的安全性，给出了安全定义和形式化证明。我们构建了一个基本运算符和核心模块库，使用原子级组件设计作为编码器和解码器的基本构建块。此外，这些组件可以作为其他类似transformer的模型的基础操作符。广泛的实验评估证明了Secformer的卓越性能，同时保护了隐私，并为类似transformer的模型提供了普遍的适应性。

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引用次数: 0

A novel deviation measurement for scheduled intelligent transportation system via comparative spatial-temporal path networks 通过比较时空路径网络测量预定智能交通系统的新型偏差

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2024.04.002

Daozhong Feng , Jiajian Lai , Wenxuan Wei , Bin Hao

Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status. However, the presentation of the data lacks structural information. Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously. Therefore, there is a need for complementary methods to address these deficiencies. To address these limitations, this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system. A dual information network is constructed to assess the degree of operational deviation considering the planning tasks. To validate the effectiveness, discussions are conducted through a modified cosine similarity calculation on theoretical analysis, delay level description, and the ability to identify abnormal dates. Compared to some state-of-the-art methods, the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477. Furthermore, case analyses are invested in regions of China's Mainland, Europe, and the United States, investigating both the overall and sub-regional network fluctuations. To represent the impact of network fluctuations in sub-regions, a response loss value was developed. The times that are prone to fluctuations are also discussed through the classification of time series data. The research can offer a novel approach to system monitoring, providing a research direction that utilizes individual data combined to represent macroscopic states. Our code will be released at https://github.com/daozhong/STPN.git.

交通管理人员可以使用智能交通系统技术访问大量数据来监控网络状态。然而，数据的表示缺乏结构信息。现有的单网络描述技术无法同时表示时空特征。因此，需要补充方法来解决这些不足。为了解决这些限制，本文提出了一种将网络快照和时序路径相结合的方法。在考虑规划任务的情况下，构建了双信息网络来评估操作偏差程度。为了验证其有效性，通过改进余弦相似度计算对理论分析、延迟级别描述和异常日期识别能力进行了讨论。与现有方法相比，本文方法的平均Spearman延迟相关系数为0.847，相对距离为3.477。此外，在中国大陆、欧洲和美国地区进行了案例分析，调查了整体和次区域网络波动。为了表示分区域网络波动的影响，制定了响应损失值。通过对时间序列数据的分类，讨论了易发生波动的时间。该研究为系统监测提供了一种新的方法，提供了利用个体数据组合来表示宏观状态的研究方向。我们的代码将在https://github.com/daozhong/STPN.git上发布。

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引用次数: 0